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tensoreflusso:: ops:: Dove3
#include <math_ops.h> Seleziona gli elementi da x y , a seconda della condition .
Riepilogo
I tensori x e y devono avere tutti la stessa forma e anche l'output avrà quella forma.
Il tensore condition deve essere scalare se x y scalari. Se x e y sono vettori o di rango superiore, condition deve essere uno scalare, un vettore con dimensioni corrispondenti alla prima dimensione di x oppure deve avere la stessa forma di x .
Il tensore condition agisce come una maschera che sceglie, in base al valore di ciascun elemento, se l'elemento/riga corrispondente nell'output deve essere preso da x (se vero) o y (se falso).
Se condition è un vettore e x sono matrici y rango superiore, sceglie quale riga (dimensione esterna) copiare y x . Se condition ha la stessa forma di x e y , sceglie quale elemento copiare da x e y .
Per esempio:
# 'condition' tensor is [[True, False]
# [False, True]]
# 't' is [[1, 2],
# [3, 4]]
# 'e' is [[5, 6],
# [7, 8]]
select(condition, t, e) # => [[1, 6], [7, 4]]
# 'condition' tensor is [True, False]
# 't' is [[1, 2],
# [3, 4]]
# 'e' is [[5, 6],
# [7, 8]]
select(condition, t, e) ==> [[1, 2],
[7, 8]]
Arguments:
- scope: A Scope object
- x: = A
Tensor which may have the same shape as condition. If condition is rank 1, x may have higher rank, but its first dimension must match the size of condition.
- y: = A
Tensor with the same type and shape as x.
Returns:
Public attributes
Funzioni pubbliche
nodo
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operatore::tensorflow::Output
operator::tensorflow::Output() const
Salvo quando diversamente specificato, i contenuti di questa pagina sono concessi in base alla licenza Creative Commons Attribution 4.0, mentre gli esempi di codice sono concessi in base alla licenza Apache 2.0. Per ulteriori dettagli, consulta le norme del sito di Google Developers. Java è un marchio registrato di Oracle e/o delle sue consociate.
Ultimo aggiornamento 2025-07-26 UTC.
[null,null,["Ultimo aggiornamento 2025-07-26 UTC."],[],[],null,["# tensorflow::ops::Where3 Class Reference\n\ntensorflow::ops::Where3\n=======================\n\n`#include \u003cmath_ops.h\u003e`\n\nSelects elements from `x` or `y`, depending on `condition`.\n\nSummary\n-------\n\nThe `x`, and `y` tensors must all have the same shape, and the output will also have that shape.\n\nThe `condition` tensor must be a scalar if `x` and `y` are scalars. If `x` and `y` are vectors or higher rank, then `condition` must be either a scalar, a vector with size matching the first dimension of `x`, or must have the same shape as `x`.\n\nThe `condition` tensor acts as a mask that chooses, based on the value at each element, whether the corresponding element / row in the output should be taken from `x` (if true) or `y` (if false).\n\nIf `condition` is a vector and `x` and `y` are higher rank matrices, then it chooses which row (outer dimension) to copy from `x` and `y`. If `condition` has the same shape as `x` and `y`, then it chooses which element to copy from `x` and `y`.\n\nFor example:\n\n\n```text\n# 'condition' tensor is [[True, False]\n# [False, True]]\n# 't' is [[1, 2],\n# [3, 4]]\n# 'e' is [[5, 6],\n# [7, 8]]\nselect(condition, t, e) # =\u003e [[1, 6], [7, 4]]\n```\n\n\u003cbr /\u003e\n\n\n```text\n# 'condition' tensor is [True, False]\n# 't' is [[1, 2],\n# [3, 4]]\n# 'e' is [[5, 6],\n# [7, 8]]\nselect(condition, t, e) ==\u003e [[1, 2],\n [7, 8]]\n```\n\n\u003cbr /\u003e\n\n\n````gdscript\n \n Arguments:\n \n- scope: A /versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope object\n\n \n- x: = A /versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor which may have the same shape as condition. If condition is rank 1, x may have higher rank, but its first dimension must match the size of condition.\n\n \n- y: = A /versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor with the same type and shape as x.\n\n \n\n Returns:\n \n- /versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output: = A /versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor with the same type and shape as x and y. \n\n \n\n \n\n\n \n### Constructors and Destructors\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_where3_1a1e043e7f8493b555a94d106084a64a32(const ::/versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope & scope, ::/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input condition, ::/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input x, ::/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input y)\n \n\n \n\n\n \n\n\n \n### Public attributes\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_where3_1a9b749e1046fbe4c39075a2b037391cf2\n \n\n \n\n /versions/r2.3/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation\n \n\n \n\n\n\n #classtensorflow_1_1ops_1_1_where3_1a07742c7ad2705b0fa9b9cc9e59eca41b\n \n\n \n\n ::/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output\n \n\n \n\n\n \n\n\n \n### Public functions\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_where3_1aacfd2a5bd041b46bc2179d3e9ac5c0c6() const \n \n\n \n\n ::tensorflow::Node *\n \n\n \n\n\n\n #classtensorflow_1_1ops_1_1_where3_1a7fcabeeb211b239288d028b587a88e54() const \n \n\n \n\n `\n` \n`\n` \n\n\n\n #classtensorflow_1_1ops_1_1_where3_1aedd6e529c7127af0c5af333ded627ab3() const \n \n\n \n\n `\n` \n`\n` \n\n\n Public attributes\n \n \n### operation\n\n\n \n```\nOperation operation\n```\n\n \n\n \n \n \n### output\n\n\n \n\n\n```text\n::tensorflow::Output output\n```\n\n \n\n \n Public functions\n \n \n### Where3\n\n\n \n\n\n```gdscript\n Where3(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input condition,\n ::tensorflow::Input x,\n ::tensorflow::Input y\n)\n```\n\n \n\n \n \n \n### node\n\n\n \n\n\n```gdscript\n::tensorflow::Node * node() const \n```\n\n \n\n \n \n \n### operator::tensorflow::Input\n\n\n \n\n\n```gdscript\n operator::tensorflow::Input() const \n```\n\n \n\n \n \n \n### operator::tensorflow::Output\n\n\n \n\n\n```gdscript\n operator::tensorflow::Output() const \n```\n\n \n\n \n\n \n\n \n````"]]
